



"""simple docstring"""

import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


A_						:    Any				=      get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
							from transformers.models.mbart.modeling_mbart import shift_tokens_right

A_						:    Optional[int]				=      25_0004
A_						:    Any				=      25_0020
@require_sentencepiece
@require_tokenizers
class     lowerCAmelCase__						(	_UpperCAmelCase							,							unittest.TestCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		str  						= MBartTokenizer
	_SCREAMING_SNAKE_CASE	:		str  						= MBartTokenizerFast
	_SCREAMING_SNAKE_CASE	:		Optional[Any]  						= True
	_SCREAMING_SNAKE_CASE	:		List[str]  						= True





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	int:





					"""simple docstring"""


					super().setUp()

					# We have a SentencePiece fixture for testing
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     MBartTokenizer(_SCREAMING_SNAKE_CASE     ,       keep_accents=_SCREAMING_SNAKE_CASE    )
					tokenizer.save_pretrained(self.tmpdirname    )





	def 			_lowerCAmelCase     (      self				:       int    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     MBartTokenizer(_SCREAMING_SNAKE_CASE     ,       keep_accents=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							List[Any]							=     tokenizer.tokenize('This is a test'    )
					self.assertListEqual(_SCREAMING_SNAKE_CASE     ,       ['▁This', '▁is', '▁a', '▁t', 'est']    )

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE    )     ,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]     ,       )

					SCREAMING_SNAKE_CASE						:							str							=     tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
					self.assertListEqual(
					    _SCREAMING_SNAKE_CASE     ,       [
					        SPIECE_UNDERLINE + 'I',
					        SPIECE_UNDERLINE + 'was',
					        SPIECE_UNDERLINE + 'b',
					        'or',
					        'n',
					        SPIECE_UNDERLINE + 'in',
					        SPIECE_UNDERLINE + '',
					        '9',
					        '2',
					        '0',
					        '0',
					        '0',
					        ',',
					        SPIECE_UNDERLINE + 'and',
					        SPIECE_UNDERLINE + 'this',
					        SPIECE_UNDERLINE + 'is',
					        SPIECE_UNDERLINE + 'f',
					        'al',
					        's',
					        'é',
					        '.',
					    ]     ,       )
					SCREAMING_SNAKE_CASE						:							Dict							=     tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE    )
					self.assertListEqual(
					    _SCREAMING_SNAKE_CASE     ,       [
					        value + tokenizer.fairseq_offset
					        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
					        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
					    ]     ,       )

					SCREAMING_SNAKE_CASE						:							Any							=     tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE    )
					self.assertListEqual(
					    _SCREAMING_SNAKE_CASE     ,       [
					        SPIECE_UNDERLINE + 'I',
					        SPIECE_UNDERLINE + 'was',
					        SPIECE_UNDERLINE + 'b',
					        'or',
					        'n',
					        SPIECE_UNDERLINE + 'in',
					        SPIECE_UNDERLINE + '',
					        '<unk>',
					        '2',
					        '0',
					        '0',
					        '0',
					        ',',
					        SPIECE_UNDERLINE + 'and',
					        SPIECE_UNDERLINE + 'this',
					        SPIECE_UNDERLINE + 'is',
					        SPIECE_UNDERLINE + 'f',
					        'al',
					        's',
					        '<unk>',
					        '.',
					    ]     ,       )







	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	Optional[int]:





					"""simple docstring"""


					if not self.test_slow_tokenizer:
									# as we don't have a slow version, we can't compare the outputs between slow and fast versions
									return

					SCREAMING_SNAKE_CASE						:							List[Any]							=     (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
													SCREAMING_SNAKE_CASE						:							List[Any]							=     self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							str							=     self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )

													SCREAMING_SNAKE_CASE						:							Dict							=     tempfile.mkdtemp()

													SCREAMING_SNAKE_CASE						:							Any							=     tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE    )

													# Checks it save with the same files + the tokenizer.json file for the fast one
													self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files    )    )
													SCREAMING_SNAKE_CASE						:							Optional[int]							=     tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f    )
													self.assertSequenceEqual(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )

													# Checks everything loads correctly in the same way
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							List[Any]							=     tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE    )

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    )
																	# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																	# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

													shutil.rmtree(_SCREAMING_SNAKE_CASE    )

													# Save tokenizer rust, legacy_format=True
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     tempfile.mkdtemp()

													SCREAMING_SNAKE_CASE						:							Dict							=     tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE     ,       legacy_format=_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							List[str]							=     tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE    )

													# Checks it save with the same files
													self.assertSequenceEqual(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )

													# Checks everything loads correctly in the same way
													SCREAMING_SNAKE_CASE						:							Dict							=     tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							int							=     tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE    )

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    )

													shutil.rmtree(_SCREAMING_SNAKE_CASE    )

													# Save tokenizer rust, legacy_format=False
													SCREAMING_SNAKE_CASE						:							str							=     tempfile.mkdtemp()

													SCREAMING_SNAKE_CASE						:							str							=     tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE     ,       legacy_format=_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE    )

													# Checks it saved the tokenizer.json file
													self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files    )    )

													# Checks everything loads correctly in the same way
													SCREAMING_SNAKE_CASE						:							int							=     tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							Dict							=     tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE    )

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    )

													shutil.rmtree(_SCREAMING_SNAKE_CASE    )




@require_torch
@require_sentencepiece
@require_tokenizers
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		int  						= '''facebook/mbart-large-en-ro'''
	_SCREAMING_SNAKE_CASE	:		Any  						= [
	    ''' UN Chief Says There Is No Military Solution in Syria''',
	    ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
	]
	_SCREAMING_SNAKE_CASE	:		Any  						= [
	    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
	    '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
	    ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
	    ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
	]
	_SCREAMING_SNAKE_CASE	:		Tuple  						= [82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Optional[int]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     MBartTokenizer.from_pretrained(
					    cls.checkpoint_name     ,       src_lang='en_XX'     ,       tgt_lang='ro_RO'    )
					SCREAMING_SNAKE_CASE						:							str							=     1
					return cls





	def 			_lowerCAmelCase     (      self				:       int    )						->	str:





					"""simple docstring"""


					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']     ,       250_001    )
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']     ,       250_004    )
					self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']     ,       250_020    )





	def 			_lowerCAmelCase     (      self				:       int    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.tokenizer.batch_encode_plus(self.src_text    ).input_ids[0]
					self.assertListEqual(self.expected_src_tokens     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Any    )						->	Any:





					"""simple docstring"""


					self.assertIn(_SCREAMING_SNAKE_CASE     ,       self.tokenizer.all_special_ids    )
					SCREAMING_SNAKE_CASE						:							int							=     [RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
					SCREAMING_SNAKE_CASE						:							str							=     self.tokenizer.decode(_SCREAMING_SNAKE_CASE     ,       skip_special_tokens=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							int							=     self.tokenizer.decode(generated_ids[1:]     ,       skip_special_tokens=_SCREAMING_SNAKE_CASE    )
					self.assertEqual(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					self.assertNotIn(self.tokenizer.eos_token     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Dict							=     ['this is gunna be a long sentence ' * 20]
					assert isinstance(src_text[0]     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     10
					SCREAMING_SNAKE_CASE						:							int							=     self.tokenizer(_SCREAMING_SNAKE_CASE     ,       max_length=_SCREAMING_SNAKE_CASE     ,       truncation=_SCREAMING_SNAKE_CASE    ).input_ids[0]
					self.assertEqual(ids[-2]     ,       2    )
					self.assertEqual(ids[-1]     ,       _SCREAMING_SNAKE_CASE    )
					self.assertEqual(len(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	Tuple:





					"""simple docstring"""


					self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']    )     ,       [250_026, 250_001]    )





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     tempfile.mkdtemp()
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.tokenizer.fairseq_tokens_to_ids
					self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Any							=     MBartTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
					self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,       _SCREAMING_SNAKE_CASE    )





	@require_torch
	def 			_lowerCAmelCase     (      self				:       Any    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.tokenizer(self.src_text     ,       text_target=self.tgt_text     ,       padding=_SCREAMING_SNAKE_CASE     ,       return_tensors='pt'    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     shift_tokens_right(batch['labels']     ,       self.tokenizer.pad_token_id    )

					# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
					assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
					assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
					assert batch.decoder_input_ids[1][-1] == 2
					assert batch.labels[1][-2:].tolist() == [2, RO_CODE]





	@require_torch
	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.tokenizer(
					    self.src_text     ,       text_target=self.tgt_text     ,       padding=_SCREAMING_SNAKE_CASE     ,       truncation=_SCREAMING_SNAKE_CASE     ,       max_length=len(self.expected_src_tokens    )     ,       return_tensors='pt'     ,       )

					SCREAMING_SNAKE_CASE						:							Tuple							=     shift_tokens_right(batch['labels']     ,       self.tokenizer.pad_token_id    )

					self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )

					self.assertEqual((2, 14)     ,       batch.input_ids.shape    )
					self.assertEqual((2, 14)     ,       batch.attention_mask.shape    )
					SCREAMING_SNAKE_CASE						:							int							=     batch.input_ids.tolist()[0]
					self.assertListEqual(self.expected_src_tokens     ,       _SCREAMING_SNAKE_CASE    )
					self.assertEqual(2     ,       batch.decoder_input_ids[0, -1]    )  # EOS
					# Test that special tokens are reset
					self.assertEqual(self.tokenizer.prefix_tokens     ,       []    )
					self.assertEqual(self.tokenizer.suffix_tokens     ,       [self.tokenizer.eos_token_id, EN_CODE]    )





	def 			_lowerCAmelCase     (      self				:       str    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     self.tokenizer(self.src_text     ,       padding=_SCREAMING_SNAKE_CASE     ,       truncation=_SCREAMING_SNAKE_CASE     ,       max_length=3     ,       return_tensors='pt'    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     self.tokenizer(
					    text_target=self.tgt_text     ,       padding=_SCREAMING_SNAKE_CASE     ,       truncation=_SCREAMING_SNAKE_CASE     ,       max_length=10     ,       return_tensors='pt'    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     targets['input_ids']
					SCREAMING_SNAKE_CASE						:							str							=     shift_tokens_right(_SCREAMING_SNAKE_CASE     ,       self.tokenizer.pad_token_id    )

					self.assertEqual(batch.input_ids.shape[1]     ,       3    )
					self.assertEqual(batch.decoder_input_ids.shape[1]     ,       10    )







	@require_torch
	def 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     self.tokenizer._build_translation_inputs(
					    'A test'     ,       return_tensors='pt'     ,       src_lang='en_XX'     ,       tgt_lang='ar_AR'    )

					self.assertEqual(
					    nested_simplify(_SCREAMING_SNAKE_CASE    )     ,       {
					        # A, test, EOS, en_XX
					        'input_ids': [[62, 3_034, 2, 250_004]],
					        'attention_mask': [[1, 1, 1, 1]],
					        # ar_AR
					        'forced_bos_token_id': 250_001,
					    }     ,       )












"""simple docstring"""





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__   =  logging.getLogger(__name__)

@dataclass
class 						lowercase   :
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = 42







@dataclass
class 						lowercase   :
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = None
      _SCREAMING_SNAKE_CASE							      = None







class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'train'
      _SCREAMING_SNAKE_CASE							      = 'dev'
      _SCREAMING_SNAKE_CASE							      = 'test'







class 						lowercase   :





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase					)						->	List[InputExample]:
            raise NotImplementedError





      @staticmethod
      def        _snake_case     (			lowercase					)						->	List[str]:
            raise NotImplementedError





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase=False ,		lowercase="[CLS]" ,		lowercase=1 ,		lowercase="[SEP]" ,		lowercase=False ,		lowercase=False ,		lowercase=0 ,		lowercase=0 ,		lowercase=-100 ,		lowercase=0 ,		lowercase=True ,		)						->	List[InputFeatures]:
            lowerCAmelCase    		=			{label: i for i, label in enumerate(lowercase					)}

            lowerCAmelCase    		=			[]
            for ex_index, example in enumerate(lowercase					):
                  if ex_index % 10_000 == 0:
                        logger.info("""Writing example %d of %d""" ,		lowercase ,		len(lowercase					)					)

                  lowerCAmelCase    		=			[]
                  lowerCAmelCase    		=			[]
                  for word, label in zip(example.words ,		example.labels					):
                        lowerCAmelCase    		=			tokenizer.tokenize(lowercase					)

                        # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                        if len(lowercase					) > 0:
                              tokens.extend(lowercase					)
                              # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                              label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(lowercase					) - 1)					)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                  lowerCAmelCase    		=			tokenizer.num_special_tokens_to_add()
                  if len(lowercase					) > max_seq_length - special_tokens_count:
                        lowerCAmelCase    		=			tokens[: (max_seq_length - special_tokens_count)]
                        lowerCAmelCase    		=			label_ids[: (max_seq_length - special_tokens_count)]

                  # The convention in BERT is:
                  # (a) For sequence pairs:
                  #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
                  #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
                  # (b) For single sequences:
                  #  tokens:   [CLS] the dog is hairy . [SEP]
                  #  type_ids:   0   0   0   0  0     0   0
                  #
                  # Where "type_ids" are used to indicate whether this is the first
                  # sequence or the second sequence. The embedding vectors for `type=0` and
                  # `type=1` were learned during pre-training and are added to the wordpiece
                  # embedding vector (and position vector). This is not *strictly* necessary
                  # since the [SEP] token unambiguously separates the sequences, but it makes
                  # it easier for the model to learn the concept of sequences.
                  #
                  # For classification tasks, the first vector (corresponding to [CLS]) is
                  # used as the "sentence vector". Note that this only makes sense because
                  # the entire model is fine-tuned.
                  tokens += [sep_token]
                  label_ids += [pad_token_label_id]
                  if sep_token_extra:
                        # roberta uses an extra separator b/w pairs of sentences
                        tokens += [sep_token]
                        label_ids += [pad_token_label_id]
                  lowerCAmelCase    		=			[sequence_a_segment_id] * len(lowercase					)

                  if cls_token_at_end:
                        tokens += [cls_token]
                        label_ids += [pad_token_label_id]
                        segment_ids += [cls_token_segment_id]
                  else:
                        lowerCAmelCase    		=			[cls_token] + tokens
                        lowerCAmelCase    		=			[pad_token_label_id] + label_ids
                        lowerCAmelCase    		=			[cls_token_segment_id] + segment_ids

                  lowerCAmelCase    		=			tokenizer.convert_tokens_to_ids(lowercase					)

                  # The mask has 1 for real tokens and 0 for padding tokens. Only real
                  # tokens are attended to.
                  lowerCAmelCase    		=			[1 if mask_padding_with_zero else 0] * len(lowercase					)

                  # Zero-pad up to the sequence length.
                  lowerCAmelCase    		=			max_seq_length - len(lowercase					)
                  if pad_on_left:
                        lowerCAmelCase    		=			([pad_token] * padding_length) + input_ids
                        lowerCAmelCase    		=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                        lowerCAmelCase    		=			([pad_token_segment_id] * padding_length) + segment_ids
                        lowerCAmelCase    		=			([pad_token_label_id] * padding_length) + label_ids
                  else:
                        input_ids += [pad_token] * padding_length
                        input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                        segment_ids += [pad_token_segment_id] * padding_length
                        label_ids += [pad_token_label_id] * padding_length

                  assert len(lowercase					) == max_seq_length
                  assert len(lowercase					) == max_seq_length
                  assert len(lowercase					) == max_seq_length
                  assert len(lowercase					) == max_seq_length

                  if ex_index < 5:
                        logger.info("""*** Example ***"""					)
                        logger.info("""guid: %s""" ,		example.guid					)
                        logger.info("""tokens: %s""" ,		""" """.join([str(lowercase					) for x in tokens]					)					)
                        logger.info("""input_ids: %s""" ,		""" """.join([str(lowercase					) for x in input_ids]					)					)
                        logger.info("""input_mask: %s""" ,		""" """.join([str(lowercase					) for x in input_mask]					)					)
                        logger.info("""segment_ids: %s""" ,		""" """.join([str(lowercase					) for x in segment_ids]					)					)
                        logger.info("""label_ids: %s""" ,		""" """.join([str(lowercase					) for x in label_ids]					)					)

                  if "token_type_ids" not in tokenizer.model_input_names:
                        lowerCAmelCase    		=			None

                  features.append(
                      InputFeatures(
                          input_ids=lowercase ,		attention_mask=lowercase ,		token_type_ids=lowercase ,		label_ids=lowercase					)					)
            return features


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset

       class 						lowercase   (   _UpperCAmelCase   ):
             _SCREAMING_SNAKE_CASE							      = 42
             _SCREAMING_SNAKE_CASE							      = nn.CrossEntropyLoss().ignore_index





             def __init__(			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase = None ,		lowercase=False ,		lowercase = Split.train ,		)						->	List[str]:
                   # Load data features from cache or dataset file
                   lowerCAmelCase    		=			os.path.join(
                       lowercase ,		"""cached_{}_{}_{}""".format(mode.value ,		tokenizer.__class__.__name__ ,		str(lowercase					)					) ,		)

                   # Make sure only the first process in distributed training processes the dataset,
                   # and the others will use the cache.
                   lowerCAmelCase    		=			cached_features_file + """.lock"""
                   with FileLock(lowercase					):
                         if os.path.exists(lowercase					) and not overwrite_cache:
                               logger.info(f'Loading features from cached file {cached_features_file}'					)
                               lowerCAmelCase    		=			torch.load(lowercase					)
                         else:
                               logger.info(f'Creating features from dataset file at {data_dir}'					)
                               lowerCAmelCase    		=			token_classification_task.read_examples_from_file(lowercase ,		lowercase					)
                               # TODO clean up all this to leverage built-in features of tokenizers
                               lowerCAmelCase    		=			token_classification_task.convert_examples_to_features(
                                   lowercase ,		lowercase ,		lowercase ,		lowercase ,		cls_token_at_end=bool(model_type in ["""xlnet"""]					) ,		cls_token=tokenizer.cls_token ,		cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 ,		sep_token=tokenizer.sep_token ,		sep_token_extra=lowercase ,		pad_on_left=bool(tokenizer.padding_side == """left"""					) ,		pad_token=tokenizer.pad_token_id ,		pad_token_segment_id=tokenizer.pad_token_type_id ,		pad_token_label_id=self.pad_token_label_id ,		)
                               logger.info(f'Saving features into cached file {cached_features_file}'					)
                               torch.save(self.features ,		lowercase					)





             def __len__(			self					)						->	int:
                   return len(self.features					)





             def __getitem__(			self ,		lowercase					)						->	InputFeatures:
                   return self.features[i]


if is_tf_available():
       import tensorflow as tf

       class 						lowercase   :
             _SCREAMING_SNAKE_CASE							      = 42
             _SCREAMING_SNAKE_CASE							      = -100





             def __init__(			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase = None ,		lowercase=False ,		lowercase = Split.train ,		)						->	Any:
                   lowerCAmelCase    		=			token_classification_task.read_examples_from_file(lowercase ,		lowercase					)
                   # TODO clean up all this to leverage built-in features of tokenizers
                   lowerCAmelCase    		=			token_classification_task.convert_examples_to_features(
                       lowercase ,		lowercase ,		lowercase ,		lowercase ,		cls_token_at_end=bool(model_type in ["""xlnet"""]					) ,		cls_token=tokenizer.cls_token ,		cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 ,		sep_token=tokenizer.sep_token ,		sep_token_extra=lowercase ,		pad_on_left=bool(tokenizer.padding_side == """left"""					) ,		pad_token=tokenizer.pad_token_id ,		pad_token_segment_id=tokenizer.pad_token_type_id ,		pad_token_label_id=self.pad_token_label_id ,		)

                   def gen():
                         for ex in self.features:
                               if ex.token_type_ids is None:
                                     yield (
                                         {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                         ex.label_ids,
                                     )
                               else:
                                     yield (
                                         {
                                             "input_ids": ex.input_ids,
                                             "attention_mask": ex.attention_mask,
                                             "token_type_ids": ex.token_type_ids,
                                         },
                                         ex.label_ids,
                                     )

                   if "token_type_ids" not in tokenizer.model_input_names:
                         lowerCAmelCase    		=			tf.data.Dataset.from_generator(
                             lowercase ,		({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa) ,		(
                                 {"""input_ids""": tf.TensorShape([None]					), """attention_mask""": tf.TensorShape([None]					)},
                                 tf.TensorShape([None]					),
                             ) ,		)
                   else:
                         lowerCAmelCase    		=			tf.data.Dataset.from_generator(
                             lowercase ,		({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa) ,		(
                                 {
                                     """input_ids""": tf.TensorShape([None]					),
                                     """attention_mask""": tf.TensorShape([None]					),
                                     """token_type_ids""": tf.TensorShape([None]					),
                                 },
                                 tf.TensorShape([None]					),
                             ) ,		)





             def        _snake_case     (			self					)						->	List[str]:
                   lowerCAmelCase    		=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features					)					)					)

                   return self.dataset





             def __len__(			self					)						->	Optional[int]:
                   return len(self.features					)





             def __getitem__(			self ,		lowercase					)						->	InputFeatures:
                   return self.features[i]






